{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Trusted-AI/AIF360/blob/main/examples/demo_lime.ipynb)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HhAPgeq7hx7p"
   },
   "source": [
    "# This notebook demonstrates how LIME - Local Interpretable Model-Agnostic Explanations can be used with models learnt with the AIF 360 toolkit to generate explanations for model predictions.\n",
    "\n",
    "For more information on LIME, see [https://github.com/marcotcr/lime](https://github.com/marcotcr/lime)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nSWeGGygruqn",
    "outputId": "e9ac4c10-dbea-4427-fdcf-0246ca52d307"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting aif360[lime]\n",
      "  Using cached aif360-0.5.0-py3-none-any.whl (214 kB)\n",
      "Requirement already satisfied: scikit-learn>=1.0 in /Users/shoffman/opt/anaconda3/envs/aif360/lib/python3.9/site-packages (from aif360[lime]) (1.0.2)\n",
      "Requirement already satisfied: numpy>=1.16 in /Users/shoffman/opt/anaconda3/envs/aif360/lib/python3.9/site-packages (from aif360[lime]) (1.21.6)\n",
      "Requirement already satisfied: scipy>=1.2.0 in /Users/shoffman/opt/anaconda3/envs/aif360/lib/python3.9/site-packages (from aif360[lime]) (1.9.1)\n",
      "Requirement already satisfied: pandas>=0.24.0 in /Users/shoffman/opt/anaconda3/envs/aif360/lib/python3.9/site-packages (from aif360[lime]) (1.4.2)\n",
      "Requirement already satisfied: matplotlib in /Users/shoffman/opt/anaconda3/envs/aif360/lib/python3.9/site-packages (from aif360[lime]) (3.5.1)\n",
      "Requirement already satisfied: lime in /Users/shoffman/opt/anaconda3/envs/aif360/lib/python3.9/site-packages (from aif360[lime]) (0.2.0.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /Users/shoffman/opt/anaconda3/envs/aif360/lib/python3.9/site-packages (from pandas>=0.24.0->aif360[lime]) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/shoffman/opt/anaconda3/envs/aif360/lib/python3.9/site-packages (from pandas>=0.24.0->aif360[lime]) (2022.1)\n",
      "Requirement already satisfied: six>=1.5 in /Users/shoffman/opt/anaconda3/envs/aif360/lib/python3.9/site-packages (from python-dateutil>=2.8.1->pandas>=0.24.0->aif360[lime]) (1.16.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/shoffman/opt/anaconda3/envs/aif360/lib/python3.9/site-packages (from scikit-learn>=1.0->aif360[lime]) (3.1.0)\n",
      "Requirement already satisfied: joblib>=0.11 in /Users/shoffman/opt/anaconda3/envs/aif360/lib/python3.9/site-packages (from scikit-learn>=1.0->aif360[lime]) (1.1.0)\n",
      "Requirement already satisfied: scikit-image>=0.12 in /Users/shoffman/opt/anaconda3/envs/aif360/lib/python3.9/site-packages (from lime->aif360[lime]) (0.19.2)\n",
      "Requirement already satisfied: tqdm in /Users/shoffman/opt/anaconda3/envs/aif360/lib/python3.9/site-packages (from lime->aif360[lime]) (4.64.0)\n",
      "Requirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,>=6.1.0 in /Users/shoffman/opt/anaconda3/envs/aif360/lib/python3.9/site-packages (from scikit-image>=0.12->lime->aif360[lime]) (9.1.0)\n",
      "Requirement already satisfied: networkx>=2.2 in /Users/shoffman/opt/anaconda3/envs/aif360/lib/python3.9/site-packages (from scikit-image>=0.12->lime->aif360[lime]) (2.8)\n",
      "Requirement already satisfied: tifffile>=2019.7.26 in /Users/shoffman/opt/anaconda3/envs/aif360/lib/python3.9/site-packages (from scikit-image>=0.12->lime->aif360[lime]) (2022.4.26)\n",
      "Requirement already satisfied: imageio>=2.4.1 in /Users/shoffman/opt/anaconda3/envs/aif360/lib/python3.9/site-packages (from scikit-image>=0.12->lime->aif360[lime]) (2.18.0)\n",
      "Requirement already satisfied: PyWavelets>=1.1.1 in /Users/shoffman/opt/anaconda3/envs/aif360/lib/python3.9/site-packages (from scikit-image>=0.12->lime->aif360[lime]) (1.3.0)\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/shoffman/opt/anaconda3/envs/aif360/lib/python3.9/site-packages (from scikit-image>=0.12->lime->aif360[lime]) (21.3)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /Users/shoffman/opt/anaconda3/envs/aif360/lib/python3.9/site-packages (from packaging>=20.0->scikit-image>=0.12->lime->aif360[lime]) (3.0.8)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/shoffman/opt/anaconda3/envs/aif360/lib/python3.9/site-packages (from matplotlib->aif360[lime]) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Users/shoffman/opt/anaconda3/envs/aif360/lib/python3.9/site-packages (from matplotlib->aif360[lime]) (4.33.3)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/shoffman/opt/anaconda3/envs/aif360/lib/python3.9/site-packages (from matplotlib->aif360[lime]) (1.4.2)\n",
      "Installing collected packages: aif360\n",
      "Successfully installed aif360-0.5.0\n"
     ]
    }
   ],
   "source": [
    "# Install AIF360\n",
    "!pip install 'aif360[lime]'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Trc8StE3poHD",
    "outputId": "e407d552-cfb2-4344-959c-a3ff8ed16275"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Some files are missing. Downloading now.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import urllib\n",
    "import aif360\n",
    "\n",
    "# Obtain the location where it is installed\n",
    "LIB_PATH = aif360.__file__.rsplit(\"aif360\", 1)[0]\n",
    "\n",
    "# check if the data got download properly\n",
    "def check_data_or_download(destn, files, data_source_directory):\n",
    "    check = all(item in os.listdir(destn) for item in files)\n",
    "    if check:\n",
    "        print(\"Adult dataset is available for us\")\n",
    "    else:\n",
    "        print(\"Some files are missing. Downloading now.\")\n",
    "        for data_file in files:\n",
    "            _ = urllib.request.urlretrieve(data_source_directory + data_file,\n",
    "                                           os.path.join(destn, data_file))\n",
    "    \n",
    "# Download adult dataset\n",
    "data_source_directory = \"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/\"\n",
    "destn = os.path.join(LIB_PATH, \"aif360\", \"data\", \"raw\", \"adult\")\n",
    "files = [\"adult.data\", \"adult.test\", \"adult.names\"]\n",
    "\n",
    "check_data_or_download(destn, files, data_source_directory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BqPouwLYhx7v",
    "outputId": "5902d47b-0f8e-421e-e863-96c8fe20dfb2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:No module named 'tempeh': LawSchoolGPADataset will be unavailable. To install, run:\n",
      "pip install 'aif360[LawSchoolGPA]'\n",
      "WARNING:root:No module named 'fairlearn': ExponentiatedGradientReduction will be unavailable. To install, run:\n",
      "pip install 'aif360[Reductions]'\n",
      "WARNING:root:No module named 'fairlearn': GridSearchReduction will be unavailable. To install, run:\n",
      "pip install 'aif360[Reductions]'\n",
      "WARNING:root:No module named 'fairlearn': GridSearchReduction will be unavailable. To install, run:\n",
      "pip install 'aif360[Reductions]'\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import sklearn.model_selection\n",
    "import sklearn.metrics\n",
    "import sklearn.datasets\n",
    "import sklearn.ensemble\n",
    "import sklearn.preprocessing\n",
    "import numpy as np\n",
    "import lime\n",
    "import lime.lime_tabular\n",
    "from IPython.display import Markdown, display\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "sys.path.append(\"../\")\n",
    "import numpy as np\n",
    "from aif360.datasets import BinaryLabelDataset\n",
    "from aif360.metrics.binary_label_dataset_metric import BinaryLabelDatasetMetric\n",
    "from aif360.metrics.classification_metric import ClassificationMetric\n",
    "from aif360.algorithms.preprocessing.optim_preproc_helpers.data_preproc_functions import load_preproc_data_adult\n",
    "from aif360.algorithms.preprocessing.reweighing import Reweighing\n",
    "\n",
    "\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "from IPython.display import Markdown, display\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from aif360.datasets.lime_encoder import LimeEncoder \n",
    "\n",
    "\n",
    "from aif360.datasets.adult_dataset import AdultDataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7wFbwODjhx7w"
   },
   "source": [
    "**Load dataset and display statistics**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uHryVDz-hx7w",
    "outputId": "b6b5dbae-d6a4-4f3b-8ef3-c1fe23715cd4",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Missing Data: 3620 rows removed from AdultDataset.\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "\n",
    "dataset_orig = AdultDataset()\n",
    "dataset_orig_train, dataset_orig_test = dataset_orig.split([0.7], shuffle=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 75
    },
    "id": "7v9Y62B8hx7x",
    "outputId": "e73ace40-4306-4a5e-c089-f0ef428a9ec4",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "#### Original training dataset"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference in mean outcomes between privileged and unprivileged groups = -0.106968\n"
     ]
    }
   ],
   "source": [
    "# Metric for the original dataset\n",
    "sens_attr = dataset_orig_train.protected_attribute_names[0]\n",
    "sens_idx = dataset_orig_train.protected_attribute_names.index(sens_attr)\n",
    "privileged_groups =  [{sens_attr:dataset_orig_train.privileged_protected_attributes[sens_idx][0]}] \n",
    "unprivileged_groups = [{sens_attr:dataset_orig_train.unprivileged_protected_attributes[sens_idx][0]}] \n",
    "metric_orig_train = BinaryLabelDatasetMetric(dataset_orig_train, \n",
    "                                             unprivileged_groups=unprivileged_groups,\n",
    "                                             privileged_groups=privileged_groups)\n",
    "display(Markdown(\"#### Original training dataset\"))\n",
    "print(\"Difference in mean outcomes between privileged and unprivileged groups = %f\" % metric_orig_train.mean_difference())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fykDl9a0hx7y"
   },
   "source": [
    "**Transform the data using the Re-Weighing (pre-processing) algorithm**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "6xTfciOOhx7y"
   },
   "outputs": [],
   "source": [
    "RW = Reweighing(unprivileged_groups=unprivileged_groups,\n",
    "               privileged_groups=privileged_groups)\n",
    "RW.fit(dataset_orig_train)\n",
    "dataset_transf_train = RW.transform(dataset_orig_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LHqwF4p9hx7z"
   },
   "source": [
    "**Learn and test models from the transformed data using Logistic Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "g6owmI0ehx7z"
   },
   "outputs": [],
   "source": [
    "#Train model on given dataset\n",
    "\n",
    "dataset = dataset_transf_train  # data to train on\n",
    "\n",
    "scale = StandardScaler().fit(dataset.features)   # remember the scale\n",
    "\n",
    "model = LogisticRegression()        # model to learn\n",
    "\n",
    "X_train = scale.transform(dataset.features)      #apply the scale\n",
    "y_train = dataset.labels.ravel()\n",
    "\n",
    "\n",
    "model.fit(X_train, y_train, sample_weight=dataset.instance_weights)\n",
    "\n",
    "#save model\n",
    "lr_orig = model\n",
    "lr_scale_orig = scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "86uifhEkhx7z",
    "outputId": "42d04362-9a22-42bc-e589-915698bc580e",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:00<00:00, 65.56it/s]\n"
     ]
    }
   ],
   "source": [
    "#Test model on given dataset and find threshold for best balanced accuracy\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "thresh_arr = np.linspace(0.01, 0.5, 50)\n",
    "\n",
    "scale = lr_scale_orig\n",
    "\n",
    "model = lr_orig                  #model to test\n",
    "dataset = dataset_orig_test        #data to test on\n",
    "\n",
    "X_test = scale.transform(dataset.features)   #apply the same scale as applied to the training data\n",
    "y_test = dataset.labels.ravel()\n",
    "y_test_pred_prob = model.predict_proba(X_test)\n",
    "\n",
    "\n",
    "bal_acc_arr = []\n",
    "disp_imp_arr = []\n",
    "avg_odds_diff_arr = []\n",
    "    \n",
    "for thresh in tqdm(thresh_arr):\n",
    "    y_test_pred = (y_test_pred_prob[:,1] > thresh).astype(np.double)\n",
    "\n",
    "    dataset_pred = dataset.copy()\n",
    "    dataset_pred.labels = y_test_pred\n",
    "\n",
    "    classified_metric = ClassificationMetric(dataset, \n",
    "                                                 dataset_pred,\n",
    "                                                 unprivileged_groups=unprivileged_groups,\n",
    "                                                 privileged_groups=privileged_groups)\n",
    "    metric_pred = BinaryLabelDatasetMetric(dataset_pred,\n",
    "                                                 unprivileged_groups=unprivileged_groups,\n",
    "                                                 privileged_groups=privileged_groups)\n",
    "    \n",
    "    TPR = classified_metric.true_positive_rate()\n",
    "    TNR = classified_metric.true_negative_rate()\n",
    "    bal_acc = 0.5*(TPR+TNR)\n",
    "    \n",
    "    acc = accuracy_score(y_true=dataset.labels,\n",
    "                            y_pred=dataset_pred.labels)\n",
    "    bal_acc_arr.append(bal_acc)\n",
    "    avg_odds_diff_arr.append(classified_metric.average_odds_difference())\n",
    "    disp_imp_arr.append(metric_pred.disparate_impact())\n",
    "    \n",
    "thresh_arr_best_ind = np.where(bal_acc_arr == np.max(bal_acc_arr))[0][0]\n",
    "thresh_arr_best = np.array(thresh_arr)[thresh_arr_best_ind]\n",
    "\n",
    "best_bal_acc = bal_acc_arr[thresh_arr_best_ind]\n",
    "disp_imp_at_best_bal_acc = np.abs(1.0-np.array(disp_imp_arr))[thresh_arr_best_ind]\n",
    "\n",
    "avg_odds_diff_at_best_bal_acc = avg_odds_diff_arr[thresh_arr_best_ind]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 451
    },
    "id": "yvB-9_jxhx70",
    "outputId": "f8996ab7-d50d-444c-e2fd-a0667cababd9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot balanced accuracy, abs(1-disparate impact)\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(10,7))\n",
    "ax1.plot(thresh_arr, bal_acc_arr)\n",
    "ax1.set_xlabel('Classification Thresholds', fontsize=16, fontweight='bold')\n",
    "ax1.set_ylabel('Balanced Accuracy', color='b', fontsize=16, fontweight='bold')\n",
    "ax1.xaxis.set_tick_params(labelsize=14)\n",
    "ax1.yaxis.set_tick_params(labelsize=14)\n",
    "\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(thresh_arr, np.abs(1.0-np.array(disp_imp_arr)), color='r')\n",
    "ax2.set_ylabel('abs(1-disparate impact)', color='r', fontsize=16, fontweight='bold')\n",
    "\n",
    "ax2.axvline(np.array(thresh_arr)[thresh_arr_best_ind], \n",
    "            color='k', linestyle=':')\n",
    "ax2.yaxis.set_tick_params(labelsize=14)\n",
    "ax2.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 451
    },
    "id": "yUHA0Q0Ohx70",
    "outputId": "ac335ba7-f6c1-43b6-b11e-fddee23d9f48"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot average odds difference\n",
    "fig, ax1 = plt.subplots(figsize=(10,7))\n",
    "ax1.plot(thresh_arr, bal_acc_arr)\n",
    "ax1.set_xlabel('Classification Thresholds', fontsize=16, fontweight='bold')\n",
    "ax1.set_ylabel('Balanced Accuracy', color='b', fontsize=16, fontweight='bold')\n",
    "ax1.xaxis.set_tick_params(labelsize=14)\n",
    "ax1.yaxis.set_tick_params(labelsize=14)\n",
    "\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(thresh_arr, avg_odds_diff_arr, color='r')\n",
    "ax2.set_ylabel('avg. odds diff.', color='r', fontsize=16, fontweight='bold')\n",
    "\n",
    "ax2.axvline(np.array(thresh_arr)[thresh_arr_best_ind], color='k', linestyle=':')\n",
    "ax2.yaxis.set_tick_params(labelsize=14)\n",
    "ax2.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pDmyXk1thx71",
    "outputId": "e923d5b0-2795-4bfb-9eb1-bff6afa15b64",
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold corresponding to Best balance accuracy: 0.1900\n",
      "Best balance accuracy: 0.8246\n",
      "Corresponding abs(1-disparate impact) value: 0.2480\n",
      "Corresponding average odds difference value: -0.0233\n"
     ]
    }
   ],
   "source": [
    "rf_thresh_arr_orig_best = thresh_arr_best\n",
    "print(\"Threshold corresponding to Best balance accuracy: %6.4f\" % rf_thresh_arr_orig_best)\n",
    "rf_best_bal_acc_arr_orig = best_bal_acc\n",
    "print(\"Best balance accuracy: %6.4f\" % rf_best_bal_acc_arr_orig)\n",
    "rf_disp_imp_at_best_bal_acc_orig = disp_imp_at_best_bal_acc\n",
    "print(\"Corresponding abs(1-disparate impact) value: %6.4f\" % rf_disp_imp_at_best_bal_acc_orig)\n",
    "rf_avg_odds_diff_at_best_bal_acc_orig = avg_odds_diff_at_best_bal_acc\n",
    "print(\"Corresponding average odds difference value: %6.4f\" % rf_avg_odds_diff_at_best_bal_acc_orig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bAJnNOXshx71"
   },
   "source": [
    "** Use LIME to generate explanations for predictions made using the learnt Logistic Regression model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 599
    },
    "id": "EmkD3XOdhx71",
    "outputId": "f7cc635a-e24a-4b77-8c1a-5ea0e51c8734",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold corresponding to Best balance accuracy: 0.1900\n",
      "        Actual label: [1.]\n",
      "        Actual label: [0.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "limeData = LimeEncoder().fit(dataset_orig_train)\n",
    "s_train = limeData.transform(dataset_orig_train.features)\n",
    "s_test = limeData.transform(dataset_orig_test.features)\n",
    "\n",
    "scale = lr_scale_orig\n",
    "\n",
    "model = lr_orig                  #model to test\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "explainer = lime.lime_tabular.LimeTabularExplainer(s_train ,class_names=limeData.s_class_names, \n",
    "                                                   feature_names = limeData.s_feature_names,\n",
    "                                                   categorical_features=limeData.s_categorical_features, \n",
    "                                                   categorical_names=limeData.s_categorical_names, \n",
    "                                                   kernel_width=3, verbose=False,discretize_continuous=True)\n",
    "\n",
    "s_predict_fn = lambda x: model.predict_proba(scale.transform(limeData.inverse_transform(x)))\n",
    "\n",
    "import random\n",
    "print(\"Threshold corresponding to Best balance accuracy: %6.4f\" % rf_thresh_arr_orig_best)\n",
    "i1 = 1\n",
    "exp = explainer.explain_instance(s_test[i1], s_predict_fn, num_features=5)\n",
    "exp.as_pyplot_figure()\n",
    "print(\"        Actual label: \" + str(dataset_orig_test.labels[i1]))\n",
    "\n",
    "i2 = 100\n",
    "exp = explainer.explain_instance(s_test[i2], s_predict_fn, num_features=5)\n",
    "exp.as_pyplot_figure()\n",
    "print(\"        Actual label: \" + str(dataset_orig_test.labels[i2]))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RGObx7Sehx72"
   },
   "source": [
    "**Learn and test models from the transformed data using Random Forests**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "EyLIu7VXhx72"
   },
   "outputs": [],
   "source": [
    "#Train model on given dataset\n",
    "\n",
    "dataset = dataset_transf_train  # data to train on\n",
    "\n",
    "scale = StandardScaler().fit(dataset.features)   # remember the scale\n",
    "\n",
    "model = sklearn.ensemble.RandomForestClassifier(n_estimators=500)       # model to learn\n",
    "\n",
    "X_train = scale.transform(dataset.features)      #apply the scale\n",
    "y_train = dataset.labels.ravel()\n",
    "\n",
    "\n",
    "model.fit(X_train, y_train, sample_weight=dataset.instance_weights)\n",
    "\n",
    "#save model\n",
    "rf_orig = model\n",
    "rf_scale_orig = scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BTAwPVvKhx72",
    "outputId": "4466b094-51f4-4059-adb1-c6ae84a4e78e",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:01<00:00, 44.70it/s]\n"
     ]
    }
   ],
   "source": [
    "#Test model on given dataset and find threshold for best balanced accuracy\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "thresh_arr = np.linspace(0.01, 0.5, 50)\n",
    "\n",
    "scale = rf_scale_orig\n",
    "\n",
    "model = rf_orig                  #model to test\n",
    "dataset = dataset_orig_test        #data to test on\n",
    "\n",
    "X_test = scale.transform(dataset.features)   #apply the same scale as applied to the training data\n",
    "y_test = dataset.labels.ravel()\n",
    "y_test_pred_prob = model.predict_proba(X_test)\n",
    "\n",
    "\n",
    "bal_acc_arr = []\n",
    "disp_imp_arr = []\n",
    "avg_odds_diff_arr = []\n",
    "    \n",
    "for thresh in tqdm(thresh_arr):\n",
    "    y_test_pred = (y_test_pred_prob[:,1] > thresh).astype(np.double)\n",
    "\n",
    "    dataset_pred = dataset.copy()\n",
    "    dataset_pred.labels = y_test_pred\n",
    "\n",
    "    classified_metric = ClassificationMetric(dataset, \n",
    "                                                 dataset_pred,\n",
    "                                                 unprivileged_groups=unprivileged_groups,\n",
    "                                                 privileged_groups=privileged_groups)\n",
    "    metric_pred = BinaryLabelDatasetMetric(dataset_pred,\n",
    "                                                 unprivileged_groups=unprivileged_groups,\n",
    "                                                 privileged_groups=privileged_groups)\n",
    "    \n",
    "    TPR = classified_metric.true_positive_rate()\n",
    "    TNR = classified_metric.true_negative_rate()\n",
    "    bal_acc = 0.5*(TPR+TNR)\n",
    "    \n",
    "    acc = accuracy_score(y_true=dataset.labels,\n",
    "                            y_pred=dataset_pred.labels)\n",
    "    bal_acc_arr.append(bal_acc)\n",
    "    avg_odds_diff_arr.append(classified_metric.average_odds_difference())\n",
    "    disp_imp_arr.append(metric_pred.disparate_impact())\n",
    "    \n",
    "thresh_arr_best_ind = np.where(bal_acc_arr == np.max(bal_acc_arr))[0][0]\n",
    "thresh_arr_best = np.array(thresh_arr)[thresh_arr_best_ind]\n",
    "\n",
    "best_bal_acc = bal_acc_arr[thresh_arr_best_ind]\n",
    "disp_imp_at_best_bal_acc = np.abs(1.0-np.array(disp_imp_arr))[thresh_arr_best_ind]\n",
    "\n",
    "avg_odds_diff_at_best_bal_acc = avg_odds_diff_arr[thresh_arr_best_ind]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 451
    },
    "id": "HVA84O7ghx72",
    "outputId": "46b38e4f-2961-4083-99ac-98f72884a5a2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot balanced accuracy, abs(1-disparate impact)\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(10,7))\n",
    "ax1.plot(thresh_arr, bal_acc_arr)\n",
    "ax1.set_xlabel('Classification Thresholds', fontsize=16, fontweight='bold')\n",
    "ax1.set_ylabel('Balanced Accuracy', color='b', fontsize=16, fontweight='bold')\n",
    "ax1.xaxis.set_tick_params(labelsize=14)\n",
    "ax1.yaxis.set_tick_params(labelsize=14)\n",
    "\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(thresh_arr, np.abs(1.0-np.array(disp_imp_arr)), color='r')\n",
    "ax2.set_ylabel('abs(1-disparate impact)', color='r', fontsize=16, fontweight='bold')\n",
    "\n",
    "ax2.axvline(np.array(thresh_arr)[thresh_arr_best_ind], \n",
    "            color='k', linestyle=':')\n",
    "ax2.yaxis.set_tick_params(labelsize=14)\n",
    "ax2.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 451
    },
    "id": "eNI4rbGyhx73",
    "outputId": "74770c36-e485-467b-d2d6-b25b61e484e0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot average odds difference\n",
    "fig, ax1 = plt.subplots(figsize=(10,7))\n",
    "ax1.plot(thresh_arr, bal_acc_arr)\n",
    "ax1.set_xlabel('Classification Thresholds', fontsize=16, fontweight='bold')\n",
    "ax1.set_ylabel('Balanced Accuracy', color='b', fontsize=16, fontweight='bold')\n",
    "ax1.xaxis.set_tick_params(labelsize=14)\n",
    "ax1.yaxis.set_tick_params(labelsize=14)\n",
    "\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(thresh_arr, avg_odds_diff_arr, color='r')\n",
    "ax2.set_ylabel('avg. odds diff.', color='r', fontsize=16, fontweight='bold')\n",
    "\n",
    "ax2.axvline(np.array(thresh_arr)[thresh_arr_best_ind], color='k', linestyle=':')\n",
    "ax2.yaxis.set_tick_params(labelsize=14)\n",
    "ax2.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yYOhiN-Hhx73",
    "outputId": "8fc6f38d-2ee7-46a3-a3be-bcb24149d0fe",
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold corresponding to Best balance accuracy: 0.1700\n",
      "Best balance accuracy: 0.8082\n",
      "Corresponding abs(1-disparate impact) value: 0.3721\n",
      "Corresponding average odds difference value: -0.0591\n"
     ]
    }
   ],
   "source": [
    "rf_thresh_arr_orig_best = thresh_arr_best\n",
    "print(\"Threshold corresponding to Best balance accuracy: %6.4f\" % rf_thresh_arr_orig_best)\n",
    "rf_best_bal_acc_arr_orig = best_bal_acc\n",
    "print(\"Best balance accuracy: %6.4f\" % rf_best_bal_acc_arr_orig)\n",
    "rf_disp_imp_at_best_bal_acc_orig = disp_imp_at_best_bal_acc\n",
    "print(\"Corresponding abs(1-disparate impact) value: %6.4f\" % rf_disp_imp_at_best_bal_acc_orig)\n",
    "rf_avg_odds_diff_at_best_bal_acc_orig = avg_odds_diff_at_best_bal_acc\n",
    "print(\"Corresponding average odds difference value: %6.4f\" % rf_avg_odds_diff_at_best_bal_acc_orig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oVuL5N1xhx73"
   },
   "source": [
    "** Use LIME to generate explanations for predictions made using the learnt Logistic Regression model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 599
    },
    "id": "3I-6CsfAhx74",
    "outputId": "0190d6f5-a8b7-4d96-8582-b5c355951b29",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold corresponding to Best balance accuracy: 0.1700\n",
      "        Actual label: [1.]\n",
      "        Actual label: [0.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "limeData = LimeEncoder().fit(dataset_orig_train)\n",
    "s_train = limeData.transform(dataset_orig_train.features)\n",
    "s_test = limeData.transform(dataset_orig_test.features)\n",
    "\n",
    "scale = rf_scale_orig\n",
    "\n",
    "model = rf_orig                  #model to test\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "explainer = lime.lime_tabular.LimeTabularExplainer(s_train ,class_names=limeData.s_class_names, \n",
    "                                                   feature_names = limeData.s_feature_names,\n",
    "                                                   categorical_features=limeData.s_categorical_features, \n",
    "                                                   categorical_names=limeData.s_categorical_names, \n",
    "                                                   kernel_width=3, verbose=False,discretize_continuous=True)\n",
    "\n",
    "s_predict_fn = lambda x: model.predict_proba(scale.transform(limeData.inverse_transform(x)))\n",
    "\n",
    "import random\n",
    "print(\"Threshold corresponding to Best balance accuracy: %6.4f\" % rf_thresh_arr_orig_best)\n",
    "\n",
    "exp = explainer.explain_instance(s_test[i1], s_predict_fn, num_features=5)\n",
    "exp.as_pyplot_figure()\n",
    "print(\"        Actual label: \" + str(dataset_orig_test.labels[i1]))\n",
    "\n",
    "\n",
    "exp = explainer.explain_instance(s_test[i2], s_predict_fn, num_features=5)\n",
    "exp.as_pyplot_figure()\n",
    "print(\"        Actual label: \" + str(dataset_orig_test.labels[i2]))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "vHZ17mwshx74"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-UqB9bFjhx74"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 ('aif360')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "vscode": {
   "interpreter": {
    "hash": "d0c5ced7753e77a483fec8ff7063075635521cce6e0bd54998c8f174742209dd"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
